Automatic speech recognition technology typically utilizes a corpus to translate speech data into text data. A corpus is a database of speech audio files and text transcriptions in a format that can be used to form acoustic models. A speech recognition engine may use one or more acoustic models to perform text transcriptions from speech data received from a given user. When an acoustic model is tailored for a particular speaker, the number of errors in a text transcription is relatively low. When an acoustic model is designed for a general class of speakers, however, the number of transcription errors tends to rise for a given speaker. To avoid this, some automatic speech recognition systems implement adaptation techniques to tailor a general acoustic model to a specific speaker. Adaptation techniques may involve receiving training data or testing data from a particular speaker, and either adapts an acoustic model to better match the data, or alternatively, adapts the data to match the acoustic model. The former is generally referred to as “model space adaptation” while the latter is referred to as “feature space adaption.” Model space adaptation and feature space adaptation are two different ways to apply adaptation techniques and are generally mathematically equivalent. Conventional solutions for implementing model space adaptation and feature space adaptation, however, are relatively complex and therefore typically expensive to implement. Consequently, improvements in these and other adaptation techniques are desirable. It is with respect to these and other considerations that the present improvements have been needed.